Instructions to use zhongzero/outdreamer_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zhongzero/outdreamer_model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zhongzero/outdreamer_model", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
OutDreamer checkpoint for video outpainting
This repository provides the OutDreamer checkpoint for OutDreamer: Video Outpainting with a Diffusion Transformer.
OutDreamer is a DiT-based video outpainting framework designed to extend video content beyond the original frame boundaries while maintaining spatial and temporal consistency. The model introduces an efficient video control branch, a conditional outpainting branch, mask-driven self-attention, latent alignment loss, and a cross-video-clip refiner for long video outpainting.
The method and its results are detailed in the arXiv paper: OutDreamer: Video Outpainting with a Diffusion Transformer.
How to Use
Important: This checkpoint is intended to be used with the OutDreamer codebase and is not a standalone Hugging Face pipeline.
For project details, please refer to the OutDreamer GitHub repository: zhongzero/OutDreamer
For setup and inference scripts compatible with this checkpoint, please refer to the reproduction repository: zhongzero/OutDreamer-unofficial
Citation
If you find this work helpful for your research, please cite:
@article{zhong2026outdreamer,
title={Outdreamer: Video outpainting with a diffusion transformer},
author={Zhong, Linhao and Li, Fan and Huang, Yi and Liu, Jianzhuang and Pei, Renjing and Song, Fenglong},
journal={IEEE Transactions on Image Processing},
year={2026},
publisher={IEEE}
}
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